Attribute Noise in Tabular Classification: Noise-Matched Training and the Train–Test Noise Mismatch on Public Benchmarks

Attribute noise, the measurement error in feature values, is a data-related challenge distinct from label noise, and the noise present at training time rarely equals the noise present at deployment. This article studies this train–test noise mismatch in tabular classification. Its main contribution is noise-matched training (NMT), which estimates a feature-wise noise level from the variance excess of unlabeled deployment inputs over the training inputs, with a threshold calibrated on the training split, and injects noise of that level in one additional fit. Its identifiability assumption, that the clean deployment variance equals the training variance, is stated and tested. In a one-dimensional Gaussian model, the excess risk of a rule tuned at the wrong noise level vanishes only at the matched level. Five learners on ten public OpenML/UCI datasets were evaluated under eight test-time noise conditions and twelve training conditions, with the dataset as the unit of inference. The matched level was, on average, the best fixed training level, except for nearest-neighbor classification. NMT had the highest average accuracy and helped most when only some features were noisy, but none of its dataset-level comparisons remained significant after multiplicity adjustment. It gave no benefit under impulsive noise, for nearest neighbors, or under covariate shift without noise.